Nuclei instance segmentation on histopathology images is of great clinical
value for disease analysis. Generally, fully-supervised algorithms for this
task require pixel-wise manual annotations, which is especially time-consuming
and laborious for the high nuclei density. To alleviate the annotation burden,
we seek to solve the problem through image-level weakly supervised learning,
which is underexplored for nuclei instance segmentation. Compared with most
existing methods using other weak annotations (scribble, point, etc.) for
nuclei instance segmentation, our method is more labor-saving. The obstacle to
using image-level annotations in nuclei instance segmentation is the lack of
adequate location information, leading to severe nuclei omission or overlaps.
In this paper, we propose a novel image-level weakly supervised method, called
cyclic learning, to solve this problem. Cyclic learning comprises a front-end
classification task and a back-end semi-supervised instance segmentation task
to benefit from multi-task learning (MTL). We utilize a deep learning
classifier with interpretability as the front-end to convert image-level labels
to sets of high-confidence pseudo masks and establish a semi-supervised
architecture as the back-end to conduct nuclei instance segmentation under the
supervision of these pseudo masks. Most importantly, cyclic learning is
designed to circularly share knowledge between the front-end classifier and the
back-end semi-supervised part, which allows the whole system to fully extract
the underlying information from image-level labels and converge to a better
optimum. Experiments on three datasets demonstrate the good generality of our
method, which outperforms other image-level weakly supervised methods for
nuclei instance segmentation, and achieves comparable performance to
fully-supervised methods.

本文提出了一种称为循环学习的新型图像水平弱监督方法，应用于核实例分割，通过将前端分类和后端半监督实例分割任务相结合，循环共享知识，从图像级标签中提取潜在信息，实现更好的优化，其在三个数据集上的实验均表现出优秀的广泛性，并取得了与完全监督方法可比较的性能。

循环学习：连接图像级标签和细胞核实例分割

Cyclic Learning: Bridging Image-level Labels and Nuclei Instance  Segmentation

Data-driven soft sensors are extensively used in industrial and chemical
processes to predict hard-to-measure process variables whose real value is
difficult to track during routine operations. The regression models used by
these sensors often require a large number of labeled examples, yet obtaining
the label information can be very expensive given the high time and cost
required by quality inspections. In this context, active learning methods can
be highly beneficial as they can suggest the most informative labels to query.
However, most of the active learning strategies proposed for regression focus
on the offline setting. In this work, we adapt some of these approaches to the
stream-based scenario and show how they can be used to select the most
informative data points. We also demonstrate how to use a semi-supervised
architecture based on orthogonal autoencoders to learn salient features in a
lower dimensional space. The Tennessee Eastman Process is used to compare the
predictive performance of the proposed approaches.

本文提出了一种将主动学习方法和半监督学习结合的数据驱动软测量方法，通过正交自编码器学习低维特征，以预测化工过程中难以测量的过程变量，实验证明，在流式数据下，该方法可以选择最具信息量的数据点，与传统离线环境相比获得更好的预测表现。